Social recommendations are becoming the hot topic of the day; New TV apps are appearing left and right, claiming social recommendations are the saviour of TV.

The goal of a recommendation solution is to get a user to take a tangible action- usually watching a new show, expanding their viewing habits. Recommendations are deployed to add depth and variety to a viewer’s habits, increasing satisfaction.

So are social recommendations any good?

We all love recommending our latest favourite TV show to our friends. But how often do we actually follow through on a friend’s recommendation? Think about the last time you recommended TV show to a friend. Did they ever tune in? It’s always satisfying when a friend does follow a recommendation, but that’s not usually what happens. The truth is, we are not friends with people just because we share the same taste in TV shows.

Recent research by TV Genius has shown that while 90% of people love to recommend shows to their friends, this is rarely the catalyst that gets someone to discover a new show. In fact, most of us don’t think friends and family significantly impact what we watch.

This means that social recommendations may not actually be the simple solution to recommendations that media mavens are raving about.

The point of a recommendations engine is the get users to take action and watch a new show. Since we don’t often take our friend’s advice, the chance of action on a social recommendation is fairly low.

Do social recommendations have a place in the recommendations mix?

Actually, they probably do.

Social recommendations may not encourage a viewer watch a new show, but they do encourage interactivity, and provide strong user behaviour data.

Social features provide a great way for viewers to interact with a programme or platform, and gather around the virtual water cooler. People will always love to give recommendations- even if they don’t necessarily listen to their friend’s suggestions.

This means that social recommendations can be used to drive TV personalisation and interactivity indirectly.

For instance, if the recommendations platform knows a user recommended “The Simpsons” to their friends, this is a very strong indication of preference. This can then be fed into the recommendation algorithms, improving the overall accuracy of the recommendation solution.

This is drastically different to the normal ecommerce landscape, where the top selling product might only be bought by 1% of customers each day.

TV recommendations instead need to be based on a variety of specially-tailored algorithms. Different types of recommendations include personal (based on user behaviour), editorial, social, contextual and sequential. By defining these elements, users can effectively be encouraged to take action.

Broadly speaking there are 3 different recommendation algorithms that can be used:

1. Collaborative filtering of different users’ behaviour, preferences, and ratings

2. Automatic content analysis and extraction of common patterns

3. Social recommendations based on personal choices from other people

However, most recommendations systems use a combination of different approaches.

By combining different types of recommendations together, the perfect mix can be found. This can help encourage consumers to watch new content- expanding the depth and variety that they watch.

Although social recommendations may not be the quick-fix easy solution that some developers hoped, this new type of recommendations definitely has a strong place in the new TV landscape.

Emma is Marketing Executive and blogger at TV Genius– keeping TV relevant with recommendations search, and enhanced TV guides since 2005. You can follow me on Twitter: @TV_Genius